Knowing the crop yield for a growing season is important for running and managing a farm. To determine crop yield, data collected from the field is typically run through an analysis tool which contains filters that ‘cleanse’ the data by removing outlying data points and eliminate errors that are integrated due to sensor calibration and mechanical defects of the machinery. It can happen that only 10% of the yield data may be collected but the yield distribution at the whole farm level may be of interest. In such situations interpolation techniques are employed where distant data points are used to create intermediary values between existing points. Interpolation techniques may, however, be inaccurate.
Even when the whole data is available for the farm, standard yield processing methods like high-pass and low-pass filters tend to eliminate a large percentage of the data collected. For instance, it is common practice to eliminate more than 20%, and in some situations the eliminated data points can be up to 80% of the data points collected. Some of the eliminated data points may be valid but are still eliminated due to the filter settings, and their order may influence how data points are eliminated. There is however a need to create a continuous distribution of the crop yield data across the farm, as this data may drive additional prescriptive services such as variable rate seeding, fertilization, management, irrigation, etc.
In order to fill in data points for regions where the data was eliminated by the filters, a simple linear interpolation or Kriging is often carried out across the remaining data points. However, since interpolation simply takes the distance between existing points and weights them, the resulting yield distribution is oftentimes not a good representation of the true yield distribution across the farm.
Therefore, there is a need to create yield maps that are as close as possible to the true yield distribution at the farm level.